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Graphing Antiderivatives01:30

Graphing Antiderivatives

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The concept of an antiderivative is fundamental in calculus, describing how a function's values accumulate over time. This process is closely related to physical motion, such as the movement of a rolling ball. As the ball progresses, its position changes in response to variations in velocity, just as an antiderivative graph reflects the cumulative effect of the original function's values.Graphing an antiderivative requires interpreting how a function's values influence the shape of its...
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The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
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Related Experiment Video

Updated: Jan 26, 2026

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures
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A graph based algorithm for generating EST consensus sequences.

Ketil Malde1, Eivind Coward, Inge Jonassen

  • 1Department of Informatics, University of Bergen, Norway. ketil@ii.uib.no

Bioinformatics (Oxford, England)
|December 2, 2004
PubMed
Summary
This summary is machine-generated.

This study introduces a novel algorithm for assembling Expressed Sequence Tags (ESTs) into consensus sequences. The new method improves accuracy and maintains competitive speed for gene discovery and alternative splicing analysis.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Expressed Sequence Tags (ESTs) are a valuable but error-prone resource in computational biology.
  • ESTs are crucial for gene discovery, identification, and the analysis of alternative splicing.
  • Reconstructing mRNA from ESTs via assembly into consensus sequences presents a significant computational challenge.

Purpose of the Study:

  • To develop and evaluate a novel algorithm for assembling EST sequences into high-quality consensus sequences.
  • To compare the performance of the proposed algorithm against existing EST assembly tools.

Main Methods:

  • The proposed algorithm constructs a graph from fixed-size sequence fragments.
  • Consensus sequences are generated by traversing this graph.
  • An experimental comparison was conducted between the new algorithm's output and those of established assemblers against known mRNA sequences.

Main Results:

  • The developed algorithm produces consensus sequences of higher quality compared to established assemblers in most cases.
  • The algorithm achieves competitive processing speeds.

Conclusions:

  • The novel graph-based algorithm offers an effective approach for EST assembly.
  • This method enhances the reliability of gene discovery and alternative splicing analysis using EST data.